Dimitrios Rakovitis
Papers
2
Total Citations
5
H-Index
2
About
Dimitrios Rakovitis is a rising researcher in robotics, specializing in adaptive control and autonomous manipulation for interactive mobile systems. His work focuses on enabling robots to safely and effectively perform real-world tasks—such as opening doors or industrial assembly—by adapting to uncertain contact dynamics and unstructured environments. In his 2024 paper on Gaussian Mixture Likelihood-based Adaptive MPC for interactive mobile manipulators (3 citations), Rakovitis introduces a novel framework that fuses probabilistic modeling with model predictive control, allowing robots to adjust their behavior in real time during physical interactions. This contribution addresses a critical gap in deploying mobile manipulators outside controlled labs. His 2025 study on continuous learning of contact episodes from proprioceptive sensors (2 citations) further advances industrial assembly by leveraging Adaptive Resonance Theory to enable robots to learn and recognize contact patterns without human intervention. Though early in his career, Rakovitis’s work demonstrates a clear trajectory toward robust, learning-enabled autonomy for interactive robotics—bridging control theory, machine learning, and practical deployment. His research is particularly relevant for students and engineers working on adaptive manipulation, human-robot interaction, and resilient industrial automation.
Research Focus
Key Achievements
Top Papers
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- 2